{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Convolutional GANs\n",
    "\n",
    "In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images; you can read the [original paper, here](https://arxiv.org/pdf/1511.06434.pdf).\n",
    "\n",
    "You'll be training DCGAN on the [Street View House Numbers](http://ufldl.stanford.edu/housenumbers/) (SVHN) dataset. These are color images of house numbers collected from Google street view. SVHN images are in color and much more variable than MNIST. \n",
    "\n",
    "<img src='assets/svhn_dcgan.png' width=80% />\n",
    "\n",
    "So, our goal is to create a DCGAN that can generate new, realistic-looking images of house numbers. We'll go through the following steps to do this:\n",
    "* Load in and pre-process the house numbers dataset\n",
    "* Define discriminator and generator networks\n",
    "* Train these adversarial networks\n",
    "* Visualize the loss over time and some sample, generated images\n",
    "\n",
    "#### Deeper Convolutional Networks\n",
    "\n",
    "Since this dataset is more complex than our MNIST data, we'll need a deeper network to accurately identify patterns in these images and be able to generate new ones. Specifically, we'll use a series of convolutional or transpose convolutional layers in the discriminator and generator. It's also necessary to use batch normalization to get these convolutional networks to train. \n",
    "\n",
    "Besides these changes in network structure, training the discriminator and generator networks should be the same as before. That is, the discriminator will alternate training on real and fake (generated) images, and the generator will aim to trick the discriminator into thinking that its generated images are real!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle as pkl\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "\n",
    "Here you can download the SVHN dataset. It's a dataset built-in to the PyTorch datasets library. We can load in training data, transform it into Tensor datatypes, then create dataloaders to batch our data into a desired size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://ufldl.stanford.edu/housenumbers/train_32x32.mat to data/train_32x32.mat\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "# Tensor transform\n",
    "transform = transforms.ToTensor()\n",
    "\n",
    "# SVHN training datasets\n",
    "svhn_train = datasets.SVHN(root='data/', split='train', download=True, transform=transform)\n",
    "\n",
    "batch_size = 128\n",
    "num_workers = 0\n",
    "\n",
    "# build DataLoaders for SVHN dataset\n",
    "train_loader = torch.utils.data.DataLoader(dataset=svhn_train,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True,\n",
    "                                          num_workers=num_workers)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Data\n",
    "\n",
    "Here I'm showing a small sample of the images. Each of these is 32x32 with 3 color channels (RGB). These are the real, training images that we'll pass to the discriminator. Notice that each image has _one_ associated, numerical label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f177e34a7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain one batch of training images\n",
    "dataiter = iter(train_loader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "# plot the images in the batch, along with the corresponding labels\n",
    "fig = plt.figure(figsize=(25, 4))\n",
    "plot_size=20\n",
    "for idx in np.arange(plot_size):\n",
    "    ax = fig.add_subplot(2, plot_size/2, idx+1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.transpose(images[idx], (1, 2, 0)))\n",
    "    # print out the correct label for each image\n",
    "    # .item() gets the value contained in a Tensor\n",
    "    ax.set_title(str(labels[idx].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing: scaling from -1 to 1\n",
    "\n",
    "We need to do a bit of pre-processing; we know that the output of our `tanh` activated generator will contain pixel values in a range from -1 to 1, and so, we need to rescale our training images to a range of -1 to 1. (Right now, they are in a range from 0-1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:  tensor(0.1647)\n",
      "Max:  tensor(0.6275)\n"
     ]
    }
   ],
   "source": [
    "# current range\n",
    "img = images[0]\n",
    "\n",
    "print('Min: ', img.min())\n",
    "print('Max: ', img.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper scale function\n",
    "def scale(x, feature_range=(-1, 1)):\n",
    "    ''' Scale takes in an image x and returns that image, scaled\n",
    "       with a feature_range of pixel values from -1 to 1. \n",
    "       This function assumes that the input x is already scaled from 0-1.'''\n",
    "    # assume x is scaled to (0, 1)\n",
    "    # scale to feature_range and return scaled x\n",
    "    min, max = feature_range\n",
    "    x = x * (max - min) + min\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled min:  tensor(-0.6706)\n",
      "Scaled max:  tensor(0.2549)\n"
     ]
    }
   ],
   "source": [
    "# scaled range\n",
    "scaled_img = scale(img)\n",
    "\n",
    "print('Scaled min: ', scaled_img.min())\n",
    "print('Scaled max: ', scaled_img.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Define the Model\n",
    "\n",
    "A GAN is comprised of two adversarial networks, a discriminator and a generator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator\n",
    "\n",
    "Here you'll build the discriminator. This is a convolutional classifier like you've built before, only without any maxpooling layers. \n",
    "* The inputs to the discriminator are 32x32x3 tensor images\n",
    "* You'll want a few convolutional, hidden layers\n",
    "* Then a fully connected layer for the output; as before, we want a sigmoid output, but we'll add that in the loss function, [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss), later\n",
    "\n",
    "<img src='assets/conv_discriminator.png' width=80%/>\n",
    "\n",
    "For the depths of the convolutional layers I suggest starting with 32 filters in the first layer, then double that depth as you add layers (to 64, 128, etc.). Note that in the DCGAN paper, they did all the downsampling using only strided convolutional layers with no maxpooling layers.\n",
    "\n",
    "You'll also want to use batch normalization with [nn.BatchNorm2d](https://pytorch.org/docs/stable/nn.html#batchnorm2d) on each layer **except** the first convolutional layer and final, linear output layer. \n",
    "\n",
    "#### Helper `conv` function \n",
    "\n",
    "In general, each layer should look something like convolution > batch norm > leaky ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a convolutional + an optional batch norm layer. We'll create these using PyTorch's [Sequential container](https://pytorch.org/docs/stable/nn.html#sequential), which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.\n",
    "\n",
    "Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for strided convolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# helper conv function\n",
    "def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    conv_layer = nn.Conv2d(in_channels, out_channels, \n",
    "                           kernel_size, stride, padding, bias=False)\n",
    "    \n",
    "    # append conv layer\n",
    "    layers.append(conv_layer)\n",
    "\n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "     \n",
    "    # using Sequential container\n",
    "    return nn.Sequential(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "\n",
    "    def __init__(self, conv_dim=32):\n",
    "        super(Discriminator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        self.conv_dim = conv_dim\n",
    "\n",
    "        # 32x32 input\n",
    "        self.conv1 = conv(3, conv_dim, 4, batch_norm=False) # first layer, no batch_norm\n",
    "        # 16x16 out\n",
    "        self.conv2 = conv(conv_dim, conv_dim*2, 4)\n",
    "        # 8x8 out\n",
    "        self.conv3 = conv(conv_dim*2, conv_dim*4, 4)\n",
    "        # 4x4 out\n",
    "        \n",
    "        # final, fully-connected layer\n",
    "        self.fc = nn.Linear(conv_dim*4*4*4, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # all hidden layers + leaky relu activation\n",
    "        out = F.leaky_relu(self.conv1(x), 0.2)\n",
    "        out = F.leaky_relu(self.conv2(out), 0.2)\n",
    "        out = F.leaky_relu(self.conv3(out), 0.2)\n",
    "        \n",
    "        # flatten\n",
    "        out = out.view(-1, self.conv_dim*4*4*4)\n",
    "        \n",
    "        # final output layer\n",
    "        out = self.fc(out)        \n",
    "        return out\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "Next, you'll build the generator network. The input will be our noise vector `z`, as before. And, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.\n",
    "\n",
    "<img src='assets/conv_generator.png' width=80% />\n",
    "\n",
    "What's new here is we'll use transpose convolutional layers to create our new images. \n",
    "* The first layer is a fully connected layer which is reshaped into a deep and narrow layer, something like 4x4x512. \n",
    "* Then, we use batch normalization and a leaky ReLU activation. \n",
    "* Next is a series of [transpose convolutional layers](https://pytorch.org/docs/stable/nn.html#convtranspose2d), where you typically halve the depth and double the width and height of the previous layer. \n",
    "* And, we'll apply batch normalization and ReLU to all but the last of these hidden layers. Where we will just apply a `tanh` activation.\n",
    "\n",
    "#### Helper `deconv` function\n",
    "\n",
    "For each of these layers, the general scheme is transpose convolution > batch norm > ReLU, and so we'll define a function to put these layers together. This function will create a sequential series of a transpose convolutional + an optional batch norm layer. We'll create these using PyTorch's Sequential container, which takes in a list of layers and creates layers according to the order that they are passed in to the Sequential constructor.\n",
    "\n",
    "Note: It is also suggested that you use a **kernel_size of 4** and a **stride of 2** for transpose convolutions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper deconv function\n",
    "def deconv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n",
    "    \"\"\"Creates a transposed-convolutional layer, with optional batch normalization.\n",
    "    \"\"\"\n",
    "    # create a sequence of transpose + optional batch norm layers\n",
    "    layers = []\n",
    "    transpose_conv_layer = nn.ConvTranspose2d(in_channels, out_channels, \n",
    "                                              kernel_size, stride, padding, bias=False)\n",
    "    # append transpose convolutional layer\n",
    "    layers.append(transpose_conv_layer)\n",
    "    \n",
    "    if batch_norm:\n",
    "        # append batchnorm layer\n",
    "        layers.append(nn.BatchNorm2d(out_channels))\n",
    "        \n",
    "    return nn.Sequential(*layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    \n",
    "    def __init__(self, z_size, conv_dim=32):\n",
    "        super(Generator, self).__init__()\n",
    "\n",
    "        # complete init function\n",
    "        \n",
    "        self.conv_dim = conv_dim\n",
    "        \n",
    "        # first, fully-connected layer\n",
    "        self.fc = nn.Linear(z_size, conv_dim*4*4*4)\n",
    "\n",
    "        # transpose conv layers\n",
    "        self.t_conv1 = deconv(conv_dim*4, conv_dim*2, 4)\n",
    "        self.t_conv2 = deconv(conv_dim*2, conv_dim, 4)\n",
    "        self.t_conv3 = deconv(conv_dim, 3, 4, batch_norm=False)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        # fully-connected + reshape \n",
    "        out = self.fc(x)\n",
    "        out = out.view(-1, self.conv_dim*4, 4, 4) # (batch_size, depth, 4, 4)\n",
    "        \n",
    "        # hidden transpose conv layers + relu\n",
    "        out = F.relu(self.t_conv1(out))\n",
    "        out = F.relu(self.t_conv2(out))\n",
    "        \n",
    "        # last layer + tanh activation\n",
    "        out = self.t_conv3(out)\n",
    "        out = F.tanh(out)\n",
    "        \n",
    "        return out\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Build complete network\n",
    "\n",
    "Define your models' hyperparameters and instantiate the discriminator and generator from the classes defined above. Make sure you've passed in the correct input arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discriminator(\n",
      "  (conv1): Sequential(\n",
      "    (0): Conv2d(3, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      "  (conv2): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (conv3): Sequential(\n",
      "    (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (fc): Linear(in_features=2048, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "Generator(\n",
      "  (fc): Linear(in_features=100, out_features=2048, bias=True)\n",
      "  (t_conv1): Sequential(\n",
      "    (0): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv2): Sequential(\n",
      "    (0): ConvTranspose2d(64, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  )\n",
      "  (t_conv3): Sequential(\n",
      "    (0): ConvTranspose2d(32, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# define hyperparams\n",
    "conv_dim = 32\n",
    "z_size = 100\n",
    "\n",
    "# define discriminator and generator\n",
    "D = Discriminator(conv_dim)\n",
    "G = Generator(z_size=z_size, conv_dim=conv_dim)\n",
    "\n",
    "print(D)\n",
    "print()\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training on GPU\n",
    "\n",
    "Check if you can train on GPU. If you can, set this as a variable and move your models to GPU. \n",
    "> Later, we'll also move any inputs our models and loss functions see (real_images, z, and ground truth labels) to GPU as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available for training. Models moved to GPU\n"
     ]
    }
   ],
   "source": [
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if train_on_gpu:\n",
    "    # move models to GPU\n",
    "    G.cuda()\n",
    "    D.cuda()\n",
    "    print('GPU available for training. Models moved to GPU')\n",
    "else:\n",
    "    print('Training on CPU.')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Discriminator and Generator Losses\n",
    "\n",
    "Now we need to calculate the losses. And this will be exactly the same as before.\n",
    "\n",
    "### Discriminator Losses\n",
    "\n",
    "> * For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_real_loss + d_fake_loss`. \n",
    "* Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n",
    "\n",
    "The losses will by binary cross entropy loss with logits, which we can get with [BCEWithLogitsLoss](https://pytorch.org/docs/stable/nn.html#bcewithlogitsloss). This combines a `sigmoid` activation function **and** and binary cross entropy loss in one function.\n",
    "\n",
    "For the real images, we want `D(real_images) = 1`. That is, we want the discriminator to classify the real images with a label = 1, indicating that these are real. The discriminator loss for the fake data is similar. We want `D(fake_images) = 0`, where the fake images are the _generator output_, `fake_images = G(z)`. \n",
    "\n",
    "### Generator Loss\n",
    "\n",
    "The generator loss will look similar only with flipped labels. The generator's goal is to get `D(fake_images) = 1`. In this case, the labels are **flipped** to represent that the generator is trying to fool the discriminator into thinking that the images it generates (fakes) are real!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def real_loss(D_out, smooth=False):\n",
    "    batch_size = D_out.size(0)\n",
    "    # label smoothing\n",
    "    if smooth:\n",
    "        # smooth, real labels = 0.9\n",
    "        labels = torch.ones(batch_size)*0.9\n",
    "    else:\n",
    "        labels = torch.ones(batch_size) # real labels = 1\n",
    "    # move labels to GPU if available     \n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    # binary cross entropy with logits loss\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss\n",
    "\n",
    "def fake_loss(D_out):\n",
    "    batch_size = D_out.size(0)\n",
    "    labels = torch.zeros(batch_size) # fake labels = 0\n",
    "    if train_on_gpu:\n",
    "        labels = labels.cuda()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    # calculate loss\n",
    "    loss = criterion(D_out.squeeze(), labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "Not much new here, but notice how I am using a small learning rate and custom parameters for the Adam optimizers, This is based on some research into DCGAN model convergence.\n",
    "\n",
    "### Hyperparameters\n",
    "\n",
    "GANs are very sensitive to hyperparameters. A lot of experimentation goes into finding the best hyperparameters such that the generator and discriminator don't overpower each other. Try out your own hyperparameters or read [the DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf) to see what worked for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "# params\n",
    "lr = 0.0002\n",
    "beta1=0.5\n",
    "beta2=0.999 # default value\n",
    "\n",
    "# Create optimizers for the discriminator and generator\n",
    "d_optimizer = optim.Adam(D.parameters(), lr, [beta1, beta2])\n",
    "g_optimizer = optim.Adam(G.parameters(), lr, [beta1, beta2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Training\n",
    "\n",
    "Training will involve alternating between training the discriminator and the generator. We'll use our functions `real_loss` and `fake_loss` to help us calculate the discriminator losses in all of the following cases.\n",
    "\n",
    "### Discriminator training\n",
    "1. Compute the discriminator loss on real, training images        \n",
    "2. Generate fake images\n",
    "3. Compute the discriminator loss on fake, generated images     \n",
    "4. Add up real and fake loss\n",
    "5. Perform backpropagation + an optimization step to update the discriminator's weights\n",
    "\n",
    "### Generator training\n",
    "1. Generate fake images\n",
    "2. Compute the discriminator loss on fake images, using **flipped** labels!\n",
    "3. Perform backpropagation + an optimization step to update the generator's weights\n",
    "\n",
    "#### Saving Samples\n",
    "\n",
    "As we train, we'll also print out some loss statistics and save some generated \"fake\" samples.\n",
    "\n",
    "**Evaluation mode**\n",
    "\n",
    "Notice that, when we call our generator to create the samples to display, we set our model to evaluation mode: `G.eval()`. That's so the batch normalization layers will use the population statistics rather than the batch statistics (as they do during training), *and* so dropout layers will operate in eval() mode; not turning off any nodes for generating samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [    1/   50] | d_loss: 1.3871 | g_loss: 0.7894\n",
      "Epoch [    1/   50] | d_loss: 0.8700 | g_loss: 2.5015\n",
      "Epoch [    2/   50] | d_loss: 1.0024 | g_loss: 1.4002\n",
      "Epoch [    2/   50] | d_loss: 1.2057 | g_loss: 1.1445\n",
      "Epoch [    3/   50] | d_loss: 0.9766 | g_loss: 1.0346\n",
      "Epoch [    3/   50] | d_loss: 0.9508 | g_loss: 0.9849\n",
      "Epoch [    4/   50] | d_loss: 1.0338 | g_loss: 1.2916\n",
      "Epoch [    4/   50] | d_loss: 0.7476 | g_loss: 1.7354\n",
      "Epoch [    5/   50] | d_loss: 0.8847 | g_loss: 1.9047\n",
      "Epoch [    5/   50] | d_loss: 0.9131 | g_loss: 2.6848\n",
      "Epoch [    6/   50] | d_loss: 0.3747 | g_loss: 2.0961\n",
      "Epoch [    6/   50] | d_loss: 0.5761 | g_loss: 1.4796\n",
      "Epoch [    7/   50] | d_loss: 1.0538 | g_loss: 2.5600\n",
      "Epoch [    7/   50] | d_loss: 0.5655 | g_loss: 1.1675\n",
      "Epoch [    8/   50] | d_loss: 1.1302 | g_loss: 0.6184\n",
      "Epoch [    8/   50] | d_loss: 0.6106 | g_loss: 1.3510\n",
      "Epoch [    9/   50] | d_loss: 0.9274 | g_loss: 2.9446\n",
      "Epoch [    9/   50] | d_loss: 0.4790 | g_loss: 2.1970\n",
      "Epoch [   10/   50] | d_loss: 0.5220 | g_loss: 2.5080\n",
      "Epoch [   10/   50] | d_loss: 0.4743 | g_loss: 1.6137\n",
      "Epoch [   11/   50] | d_loss: 0.7634 | g_loss: 2.0439\n",
      "Epoch [   11/   50] | d_loss: 0.4358 | g_loss: 2.0743\n",
      "Epoch [   12/   50] | d_loss: 0.9056 | g_loss: 2.8160\n",
      "Epoch [   12/   50] | d_loss: 0.3225 | g_loss: 2.2658\n",
      "Epoch [   13/   50] | d_loss: 0.5460 | g_loss: 2.8996\n",
      "Epoch [   13/   50] | d_loss: 0.2596 | g_loss: 2.5365\n",
      "Epoch [   14/   50] | d_loss: 0.5203 | g_loss: 2.7283\n",
      "Epoch [   14/   50] | d_loss: 0.4269 | g_loss: 2.0782\n",
      "Epoch [   15/   50] | d_loss: 0.7738 | g_loss: 1.0159\n",
      "Epoch [   15/   50] | d_loss: 0.4269 | g_loss: 1.9207\n",
      "Epoch [   16/   50] | d_loss: 0.4112 | g_loss: 2.8402\n",
      "Epoch [   16/   50] | d_loss: 0.3925 | g_loss: 1.5451\n",
      "Epoch [   17/   50] | d_loss: 0.3715 | g_loss: 1.7045\n",
      "Epoch [   17/   50] | d_loss: 0.7009 | g_loss: 3.0560\n",
      "Epoch [   18/   50] | d_loss: 0.3727 | g_loss: 2.4927\n",
      "Epoch [   18/   50] | d_loss: 0.3543 | g_loss: 2.8200\n",
      "Epoch [   19/   50] | d_loss: 0.3737 | g_loss: 2.0694\n",
      "Epoch [   19/   50] | d_loss: 0.3062 | g_loss: 2.7253\n",
      "Epoch [   20/   50] | d_loss: 0.4124 | g_loss: 1.9771\n",
      "Epoch [   20/   50] | d_loss: 0.6703 | g_loss: 2.5589\n",
      "Epoch [   21/   50] | d_loss: 0.2947 | g_loss: 1.9481\n",
      "Epoch [   21/   50] | d_loss: 0.4341 | g_loss: 2.1836\n",
      "Epoch [   22/   50] | d_loss: 0.2596 | g_loss: 2.4994\n",
      "Epoch [   22/   50] | d_loss: 0.2450 | g_loss: 2.8164\n",
      "Epoch [   23/   50] | d_loss: 0.3782 | g_loss: 3.0394\n",
      "Epoch [   23/   50] | d_loss: 0.3612 | g_loss: 2.5715\n",
      "Epoch [   24/   50] | d_loss: 0.1769 | g_loss: 3.6052\n",
      "Epoch [   24/   50] | d_loss: 0.2952 | g_loss: 3.0570\n",
      "Epoch [   25/   50] | d_loss: 0.4618 | g_loss: 4.1390\n",
      "Epoch [   25/   50] | d_loss: 0.2637 | g_loss: 3.1937\n",
      "Epoch [   26/   50] | d_loss: 0.2144 | g_loss: 2.7835\n",
      "Epoch [   26/   50] | d_loss: 0.2960 | g_loss: 3.2470\n",
      "Epoch [   27/   50] | d_loss: 0.4798 | g_loss: 2.2351\n",
      "Epoch [   27/   50] | d_loss: 0.2642 | g_loss: 3.3904\n",
      "Epoch [   28/   50] | d_loss: 0.3362 | g_loss: 3.4251\n",
      "Epoch [   28/   50] | d_loss: 0.8281 | g_loss: 4.0355\n",
      "Epoch [   29/   50] | d_loss: 0.3406 | g_loss: 2.9678\n",
      "Epoch [   29/   50] | d_loss: 0.1694 | g_loss: 3.6185\n",
      "Epoch [   30/   50] | d_loss: 0.1856 | g_loss: 2.3010\n",
      "Epoch [   30/   50] | d_loss: 0.8694 | g_loss: 3.1096\n",
      "Epoch [   31/   50] | d_loss: 0.4050 | g_loss: 1.9086\n",
      "Epoch [   31/   50] | d_loss: 0.2215 | g_loss: 2.7986\n",
      "Epoch [   32/   50] | d_loss: 1.9408 | g_loss: 9.9110\n",
      "Epoch [   32/   50] | d_loss: 0.2688 | g_loss: 3.3067\n",
      "Epoch [   33/   50] | d_loss: 0.2684 | g_loss: 3.6979\n",
      "Epoch [   33/   50] | d_loss: 0.3910 | g_loss: 2.7411\n",
      "Epoch [   34/   50] | d_loss: 0.3097 | g_loss: 2.4075\n",
      "Epoch [   34/   50] | d_loss: 0.1618 | g_loss: 3.0312\n",
      "Epoch [   35/   50] | d_loss: 0.3315 | g_loss: 2.3083\n",
      "Epoch [   35/   50] | d_loss: 0.2600 | g_loss: 4.0688\n",
      "Epoch [   36/   50] | d_loss: 0.2050 | g_loss: 2.5516\n",
      "Epoch [   36/   50] | d_loss: 0.1700 | g_loss: 3.3212\n",
      "Epoch [   37/   50] | d_loss: 0.3518 | g_loss: 3.2154\n",
      "Epoch [   37/   50] | d_loss: 0.2002 | g_loss: 2.3394\n",
      "Epoch [   38/   50] | d_loss: 0.2736 | g_loss: 3.9040\n",
      "Epoch [   38/   50] | d_loss: 0.2298 | g_loss: 2.2667\n",
      "Epoch [   39/   50] | d_loss: 0.7857 | g_loss: 2.6393\n",
      "Epoch [   39/   50] | d_loss: 0.1813 | g_loss: 3.2973\n",
      "Epoch [   40/   50] | d_loss: 0.1523 | g_loss: 3.3429\n",
      "Epoch [   40/   50] | d_loss: 0.2379 | g_loss: 3.4967\n",
      "Epoch [   41/   50] | d_loss: 0.1664 | g_loss: 4.0548\n",
      "Epoch [   41/   50] | d_loss: 0.7907 | g_loss: 2.7059\n",
      "Epoch [   42/   50] | d_loss: 0.2806 | g_loss: 3.2769\n",
      "Epoch [   42/   50] | d_loss: 0.3690 | g_loss: 4.7627\n",
      "Epoch [   43/   50] | d_loss: 0.1582 | g_loss: 3.7862\n",
      "Epoch [   43/   50] | d_loss: 0.2007 | g_loss: 1.8766\n",
      "Epoch [   44/   50] | d_loss: 0.6256 | g_loss: 4.1040\n",
      "Epoch [   44/   50] | d_loss: 0.6256 | g_loss: 4.5599\n",
      "Epoch [   45/   50] | d_loss: 0.1694 | g_loss: 3.1243\n",
      "Epoch [   45/   50] | d_loss: 0.2005 | g_loss: 2.0720\n",
      "Epoch [   46/   50] | d_loss: 0.3385 | g_loss: 3.6107\n",
      "Epoch [   46/   50] | d_loss: 0.1430 | g_loss: 3.3863\n",
      "Epoch [   47/   50] | d_loss: 2.3355 | g_loss: 4.3694\n",
      "Epoch [   47/   50] | d_loss: 0.2589 | g_loss: 3.8996\n",
      "Epoch [   48/   50] | d_loss: 0.4828 | g_loss: 2.7014\n",
      "Epoch [   48/   50] | d_loss: 0.3688 | g_loss: 3.0661\n",
      "Epoch [   49/   50] | d_loss: 3.1969 | g_loss: 7.7299\n",
      "Epoch [   49/   50] | d_loss: 0.3317 | g_loss: 4.3268\n",
      "Epoch [   50/   50] | d_loss: 0.2904 | g_loss: 2.4885\n",
      "Epoch [   50/   50] | d_loss: 0.2320 | g_loss: 2.1839\n"
     ]
    }
   ],
   "source": [
    "import pickle as pkl\n",
    "\n",
    "# training hyperparams\n",
    "num_epochs = 50\n",
    "\n",
    "# keep track of loss and generated, \"fake\" samples\n",
    "samples = []\n",
    "losses = []\n",
    "\n",
    "print_every = 300\n",
    "\n",
    "# Get some fixed data for sampling. These are images that are held\n",
    "# constant throughout training, and allow us to inspect the model's performance\n",
    "sample_size=16\n",
    "fixed_z = np.random.uniform(-1, 1, size=(sample_size, z_size))\n",
    "fixed_z = torch.from_numpy(fixed_z).float()\n",
    "\n",
    "# train the network\n",
    "for epoch in range(num_epochs):\n",
    "    \n",
    "    for batch_i, (real_images, _) in enumerate(train_loader):\n",
    "                \n",
    "        batch_size = real_images.size(0)\n",
    "        \n",
    "        # important rescaling step\n",
    "        real_images = scale(real_images)\n",
    "        \n",
    "        # ============================================\n",
    "        #            TRAIN THE DISCRIMINATOR\n",
    "        # ============================================\n",
    "        \n",
    "        d_optimizer.zero_grad()\n",
    "        \n",
    "        # 1. Train with real images\n",
    "\n",
    "        # Compute the discriminator losses on real images \n",
    "        if train_on_gpu:\n",
    "            real_images = real_images.cuda()\n",
    "        \n",
    "        D_real = D(real_images)\n",
    "        d_real_loss = real_loss(D_real)\n",
    "        \n",
    "        # 2. Train with fake images\n",
    "        \n",
    "        # Generate fake images\n",
    "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "        z = torch.from_numpy(z).float()\n",
    "        # move x to GPU, if available\n",
    "        if train_on_gpu:\n",
    "            z = z.cuda()\n",
    "        fake_images = G(z)\n",
    "        \n",
    "        # Compute the discriminator losses on fake images            \n",
    "        D_fake = D(fake_images)\n",
    "        d_fake_loss = fake_loss(D_fake)\n",
    "        \n",
    "        # add up loss and perform backprop\n",
    "        d_loss = d_real_loss + d_fake_loss\n",
    "        d_loss.backward()\n",
    "        d_optimizer.step()\n",
    "        \n",
    "        \n",
    "        # =========================================\n",
    "        #            TRAIN THE GENERATOR\n",
    "        # =========================================\n",
    "        g_optimizer.zero_grad()\n",
    "        \n",
    "        # 1. Train with fake images and flipped labels\n",
    "        \n",
    "        # Generate fake images\n",
    "        z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "        z = torch.from_numpy(z).float()\n",
    "        if train_on_gpu:\n",
    "            z = z.cuda()\n",
    "        fake_images = G(z)\n",
    "        \n",
    "        # Compute the discriminator losses on fake images \n",
    "        # using flipped labels!\n",
    "        D_fake = D(fake_images)\n",
    "        g_loss = real_loss(D_fake) # use real loss to flip labels\n",
    "        \n",
    "        # perform backprop\n",
    "        g_loss.backward()\n",
    "        g_optimizer.step()\n",
    "\n",
    "        # Print some loss stats\n",
    "        if batch_i % print_every == 0:\n",
    "            # append discriminator loss and generator loss\n",
    "            losses.append((d_loss.item(), g_loss.item()))\n",
    "            # print discriminator and generator loss\n",
    "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
    "                    epoch+1, num_epochs, d_loss.item(), g_loss.item()))\n",
    "\n",
    "    \n",
    "    ## AFTER EACH EPOCH##    \n",
    "    # generate and save sample, fake images\n",
    "    G.eval() # for generating samples\n",
    "    if train_on_gpu:\n",
    "        fixed_z = fixed_z.cuda()\n",
    "    samples_z = G(fixed_z)\n",
    "    samples.append(samples_z)\n",
    "    G.train() # back to training mode\n",
    "\n",
    "\n",
    "# Save training generator samples\n",
    "with open('train_samples.pkl', 'wb') as f:\n",
    "    pkl.dump(samples, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training loss\n",
    "\n",
    "Here we'll plot the training losses for the generator and discriminator, recorded after each epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f17062edc18>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f170646c278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Generator samples from training\n",
    "\n",
    "Here we can view samples of images from the generator. We'll look at the images we saved during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function for viewing a list of passed in sample images\n",
    "def view_samples(epoch, samples):\n",
    "    fig, axes = plt.subplots(figsize=(16,4), nrows=2, ncols=8, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        img = img.detach().cpu().numpy()\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        img = ((img +1)*255 / (2)).astype(np.uint8) # rescale to pixel range (0-255)\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((32,32,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1704036860>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = view_samples(-1, samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
